code: def train_and_infer(self, idx=0):
      results = []
      set_determinism(0)
      _readers = (None, None)
      if idx == 1:
          _readers = ("itkreader", "itkreader")
      elif idx == 2:
          _readers = ("itkreader", "nibabelreader")
      losses, best_metric = run_training_test(
          self.data_dir,
          device=self.device,
          cachedataset=idx,
          readers=_readers,
      )
      infer_metric = run_inference_test(
          self.data_dir, device=self.device
      )
  
      # check training properties
      print("losses", losses)
      print("best metric", best_metric)
      print("infer metric", infer_metric)
      self.assertTrue(
          len(glob(os.path.join(self.data_dir, "runs"))) > 0
      )
      model_file = os.path.join(self.data_dir, "best_metric_model.pth")
      self.assertTrue(os.path.exists(model_file))
  
      # check inference properties
      output_files = sorted(
          glob(
              os.path.join(self.data_dir, "output", "img*", "*.nii.gz")
          )
      )
      print(
          [
              np.mean(nib.load(output).get_fdata())
              for output in output_files
          ]
      )
      results.extend(losses)
      results.append(best_metric)
      results.append(infer_metric)
      for output in output_files:
          ave = np.mean(nib.load(output).get_fdata())
          results.append(ave)
      self.assertTrue(
          test_integration_value(
              TASK, key="losses", data=results[:6], rtol=1e-3
          )
      )
      self.assertTrue(
          test_integration_value(
              TASK, key="best_metric", data=results[6], rtol=1e-2
          )
      )
      self.assertTrue(
          test_integration_value(
              TASK, key="infer_metric", data=results[7], rtol=1e-2
          )
      )
      self.assertTrue(
          test_integration_value(
              TASK, key="output_sums", data=results[8:], rtol=5e-2
          )
      )
      return results
  
imports: from __future__ import annotations
  import os
  import shutil
  import tempfile
  import unittest
  from glob import glob
  import nibabel
  import numpy
  import torch
  import monai
  from monai.data import create_test_image_3d, decollate_batch
  from monai.inferers import sliding_window_inference
  from monai.metrics import DiceMetric
  from monai.networks import eval_mode
  from monai.networks.nets import UNet
  from monai.transforms import Activations, AsDiscrete, Compose, EnsureChannelFirstd, LoadImaged, RandCropByPosNegLabeld, RandRotate90d, SaveImage, ScaleIntensityd, Spacingd
  from monai.utils import optional_import, set_determinism
  from monai.visualize import plot_2d_or_3d_image
  from tests.testing_data.integration_answers import test_integration_value
  from tests.utils import DistTestCase, TimedCall, skip_if_quick
label_desc: Use image node_id 3 for calling the IntegrationSegmentation3D obj's underlying member method code with example usage: obj.train_and_infer(idx) and returns: results